Model comparison
GPT-4 Turbo vs Kimi K2.6
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: GPT-4 Turbo unranked; Kimi K2.6 #13
Evidence parity. GPT-4 Turbo and Kimi K2.6 share 4 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to GPT-4 Turbo; 56 to Kimi K2.6.
Updated July 13, 2026- Shared results
- 4
- GPT-4 Turbo only
- 0
- Kimi K2.6 only
- 56
- Comparable categories
- 0 / 8
Benchmark data for GPT-4 Turbo and Kimi K2.6 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-4 Turbo is priced at $10.00 input / $30.00 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. Kimi K2.6 has the larger context window at 256K, compared with 128K for GPT-4 Turbo.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-4 Turbo | Δ | Kimi K2.6 |
|---|---|---|---|
| Agentic | GPT-4 TurboNot measured | MarginNo overlap | Kimi K2.673.5 |
| Coding | GPT-4 TurboNot measured | MarginNo overlap | Kimi K2.672.6 |
| Knowledge | GPT-4 TurboNot measured | MarginNo overlap | Kimi K2.642.2 |
| Math | GPT-4 TurboNot measured | MarginNo overlap | Kimi K2.667.1 |
| Multimodal | GPT-4 TurboNot measured | MarginNo overlap | Kimi K2.679.8 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4 Turbo | Kimi K2.6 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4 Turbo$10 input / $30 output | Kimi K2.6$0.95 input / $4 output | Kimi K2.6 has the lower combined listed price. |
| Generation speedtokens per second | GPT-4 Turbo30 tok/s | Kimi K2.6Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4 Turbo2.84 s | Kimi K2.6Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4 Turbo128K | Kimi K2.6256K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic22 benchmarks
| Benchmark | GPT-4 Turbo | Kimi K2.6 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| BrowseCompSource | — | 83.2% | Not comparable |
| OSWorld-VerifiedSource | — | 73.1% | Not comparable |
| ToolathlonSource | — | 50% | Not comparable |
| MCP AtlasSource | — | 55.9% | Not comparable |
| Claw-EvalSource | — | 62.3% | Not comparable |
| DeepSearchQASource | — | 92.5% | Not comparable |
| WideResearchSource | — | 80.8% | Not comparable |
| AA Agentic IndexSource | — | 30.3% | Not comparable |
| Tau2-TelecomSource | — | 95.9% | Not comparable |
| GDPval-AASource | — | 34.5% | Not comparable |
| GDPval-AASource | — | 1190 | Not comparable |
| APEX-Agents-AASource | — | 28.5% | Not comparable |
| Gert LabsSource | — | 56.82% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| AA BriefcaseSource | — | 809 | Not comparable |
| AA AutomationBenchSource | — | 19.6% | Not comparable |
| AA EnterpriseOps-GymSource | — | 38.5% | Not comparable |
| AA Harvey LABSource | — | 0.0% | Not comparable |
| AA ITBenchSource | — | 31.2% | Not comparable |
| AA Tau3 BankingSource | — | 20.6% | Not comparable |
Coding13 benchmarks
| Benchmark | GPT-4 Turbo | Kimi K2.6 | Result |
|---|---|---|---|
| AA Coding IndexSource | 21.5% | 61.8% | Kimi K2.6 leads |
| AA-SciCodeSource | 31.9% | 53.5% | Kimi K2.6 leads |
| SWE-bench VerifiedSource | — | 80.2% | Not comparable |
| LiveCodeBenchSource | — | 89.6% | Not comparable |
| LiveCodeBench v6Source | — | 89.6% | Not comparable |
| SWE-bench ProSource | — | 58.6% | Not comparable |
| SWE MultilingualSource | — | 76.7% | Not comparable |
| SciCodeSource | — | 52.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.7% | Not comparable |
| Vibe Code BenchSource | — | 37.89% | Not comparable |
| cursorBench31Source | — | 47.6% | Not comparable |
| Terminal-Bench HardSource | — | 43.9% | Not comparable |
| AA Terminal-Bench 2.1Source | — | 65.9% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-4 Turbo | Kimi K2.6 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 7.9% | 44.2% | Kimi K2.6 leads |
| AA-HLESource | 3.3% | 35.9% | Kimi K2.6 leads |
| GPQASource | — | 90.5% | Not comparable |
| GPQA-DSource | — | 90.5% | Not comparable |
| HLESource | — | 34.7% | Not comparable |
| AA-GPQA DiamondSource | — | 91.1% | Not comparable |
| AA-Omniscience IndexSource | — | 6.4% | Not comparable |
| AA-Omniscience AccuracySource | — | 32.8% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 39.3% | Not comparable |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math5 benchmarks
Multimodal7 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-4 Turbo | Kimi K2.6 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 76.0% | Not comparable |
Frequently Asked Questions (3)
Can I compare GPT-4 Turbo and Kimi K2.6 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-4 Turbo and Kimi K2.6 today?
GPT-4 Turbo: $10.00 input / $30.00 output per 1M tokens Kimi K2.6: $0.95 input / $4.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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